A simple workflow automation tool solves 95% of the problem.
No AI required.
A good consultant should be comfortable saying:
> "This problem doesn't need AI."
That often saves substantial cost and complexity.
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5. Is the organization ready?
Even the best AI solution fails without:
Quality data
Governance
Security controls
Defined business processes
Executive sponsorship
User adoption
Ask:
What data exists?
Is it accurate?
Who owns it?
How will success be measured?
What happens if the AI is wrong?
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A Practical Framework
When someone says:
> "We should use AI."
Translate it into:
Problem → Process → Data → Decision → Outcome → AI
For example:
Problem: Rising crime incidents
↓
Process: Monitoring thousands of CCTV feeds
↓
Data: Video streams from cameras
↓
Decision: Is suspicious activity occurring?
↓
Outcome: Faster incident response
↓
AI Solution: Computer vision and intelligent surveillance analytics
This is precisely the type of reasoning behind advanced surveillance platforms such as your Guardian AI and Automated Cam Intelligence concepts, where AI is not the goal itself—the goal is improved situational awareness, faster detection, and better operational decision-making.
The most valuable question is often:
> "If AI worked perfectly tomorrow, what business result would be different?"
The answer to that question usually reveals what is really being asked. Saving Changes...
Tumelo Mapitsa Project Management| Mapitsa Cyber TechSouth Africa
A Practical Framework
When someone says:
"We should use AI."
Translate it into:
Problem → Process → Data → Decision → Outcome → AI
For example:
Problem: Rising crime incidents
Process: Monitoring thousands of CCTV feeds
Data: Video streams from cameras
Decision: Is suspicious activity occurring?
Outcome: Faster incident response
AI Solution: Computer vision and intelligent surveillance analytics
This is precisely the type of reasoning behind advanced surveillance platforms such as your Guardian AI and Automated Cam Intelligence concepts, where AI is not the goal itself—the goal is improved situational awareness, faster detection, and better operational decision-making.
The most valuable question is often:
"If AI worked perfectly tomorrow, what business result would be different?"
The answer to that question usually reveals what is really being asked. Saving Changes...
When the topic of “we should use AI” is brought to the table, it shouldn’t be translated as a technical requirement. Instead, it is a signal of competitive pressure or an unmet need for speed and efficiency. The real danger in project management begins when the team skips the business discovery phase and assumes AI is a one-size-fits-all solution. To unpack what is really being asked, it is crucial to shift the conversation away from the technology itself and focus on risk and governance. A textbook example of what goes wrong when hype bypasses project governance is the real-world Air Canada case. They deployed an AI chatbot to "improve customer service," but failed to constrain its scope and validate its data rules. The chatbot ended up inventing a fake refund policy, and the courts legally held the airline accountable for the system’s hallucination. That failure wasn't a coding issue; it was a project governance failure. It mistook interface automation for backend process maturity. This is why, to unpack the real request, the first damage-control question must always be: "If the AI makes the wrong call in this specific use case, who owns the risk, and how do we mitigate it?" I'd love to open a question for the thread: How do you manage sponsor expectations when the business analysis proves that the most efficient and secure solution to their actual problem requires NO AI at all? Saving Changes...
Misunderstanding different kinds of AI, and lumping them all together gets messy quickly. Conflating a LLM with a media generation AI can be problematic when laying out a project. There's a lot of uses for AI, and often there are different models for different applications, which makes it tough to ensure everybody is on the same page. AI has definitely meant different things to different people revolving around projects. Some people have seen it as an elegant, quick and easy solution to complex issues and problems, and something that you just snap into place for it to work. Where others understand, or maybe even harp on it's short-comings in those areas. Some people see AI as a tool that helps complete work. Others view it as a toy used to generate content. When somebody says something like "we should use AI!" To me, it's almost as if saying "We should use a word processor!" Which platform? For what process? Could we examine if that actually makes sense, or creates value for this project?
Saving Changes...
Thomas RoettoLean Six Sigma Black Belt| Mosaic Life CareSt. Joseph, Mo, United States
AI can be a trigger word to really mean I want an easy solution to the problem. What is really needed many times is to understand the problem to be solved and determine if AI can help solve the actual issues. Saving Changes...
Use AI can mean get it done faster and cheaper by management. Hence you need to unpack clearly what is 'the scope'. As noted in module 1 "Without accounting for where AI matters, projects face predictable risks" I see the "missed expectations" or non-aligned expectations on teams where some members are more comfortable using the AI. This can manifest in the disconnect between the traditional decision maker (low AI knowledge) and the responsible team (high AI knowledge). Organizations having to adapt quickly to the skills gap between the A and the R in the RACI. AI may not be universally understood by all team members which still presents communication and change challenges for us humans. Saving Changes...
We must always do an needs assessment to understand the core needs and then work forward. Saving Changes...
Michael TladiBotswana Unified Revenue ServiceMochudi, KL, Botswana
Mar 25, 2026 4:50 AM
Replying to Douglas Boyd
...
It is recognised that AI can assist, but we need to obtain clarity as to what AI system is to be used as there are many.
It starts with understanding of the problem at hand. Does it really need AI? How will AI assist? If one can answer these questions, then identification of the AI system is workable. Saving Changes...
When someone says "we should use AI", I start trying to understand what problems are we trying to solve. What is not working? What is taking too long , costing too much, or causing errors? What outcomes would be regarded as success? Most people jump into using AI as a "solution-first idea.
Then identify what job needs to be done? Thereby translating the statement of problems into specific tasks. Saving Changes...
"In Italy for thirty years under the Borgias they had warfare, terror, murder, bloodshed - but they produced Michelangelo, Leonardo da Vinci, and the Renaissance. In Switzerland they had brotherly love, 500 years of democracy and peace, and what did that produce? The cuckoo clock."
PMI Team Member
